Artificial intelligence is getting its teeth into lip reading. A project by Google's DeepMind and the University of Oxford applied deep learning to a huge data set of BBC programmes to create a lip-reading system that leaves professionals in the dust. The AI system was trained using some 5000 hours from six different TV programmes, including Newsnight, BBC Breakfast and Question Time. In total, the videos contained 118,000 sentences. First the University of Oxford and DeepMind researchers trained the AI on shows that aired between January 2010 and December 2015. Then they tested its performance on programmes broadcast between March and September 2016.

Artificial intelligence image by Sean Davis, licensed under Creative Commons Attribution 2.0 Generic (CC by 2.0) Sony announced last week at they have created two brand new pop songs. While this doesn't appear to be a great feat at first, they've actually done this using artificial intelligence. Check out the first song titled Daddy's Car created in the style of The Beatles. This song was created over at Sony CSL Research Laboratory using their Flow Machines software. How does it work, exactly?

Jason Tan is the co-founder and CEO of Sift Science. He's also held leadership and engineering roles at BuzzLabs, Optify and Zillow. Facebook recently fired the entire Trending Topics team of human editors amid accusations they were promoting specific agendas and biasing what news was deemed "important." Now the company is relying on machine learning algorithms to manage Trending Topics -- and finding that keeping the results free of hoaxes and fake news isn't always easy. The social media giant recently assured an audience at TechCrunch Disrupt that it was working on new technology that would help prevent untrue or satirical stories from being labeled as legitimate news we should follow.

You've seen it in the movies: super smart robots finally come to the conclusion that we humans are making a mess of things. Still think that's the stuff of science fiction? We're building machines that are smarter than we are, that can do our jobs for us, and that we will very soon come to depend on to maintain our technology-saturated lifestyles. With so much going on in the world of AI, we are becoming all but desensitized to the technology. So let's pause for a moment and look at the slightly creepy, but really cool advances taking place in AI today.

Amazon's Echo voice-controlled smart speaker is finally available in the UK, but was the wait worth it? The Echo is one of the first devices with Amazon's voice assistant - a rival to Apple's Siri, Google's Assistant and Microsoft's Cortana - which allows you to control music playback just by speaking to it and a whole lot more. Echo is three devices in one. It's a voice-controlled Wi-Fi and Bluetooth speaker capable of playing music from Amazon music, a Spotify premium account or a smartphone, tablet or computer connected via Bluetooth. It's also a smart voice assistant called Alexa that's capable of answering queries, setting timers, doing calculations, telling you the weather or what's in your calendar and other bits you might expect from Siri, Cortana or Google Assistant.

There aren't many companies that insist staff start work every day at such an oddly specific time as Pivotal Software. Employees at the US firm's 20 global offices all have to be at work and ready to go at exactly 9.06am. At that precise time a cowbell is rung, or a gong is hit, and all workers gather for a brief stand-up meeting that lasts for between five and 10 minutes. Then the firm's programmers hit their computers, with no other meetings or distractions for the rest of the day. Pivotal's founder and chief executive Rob Mee says it is all about making the working day as efficient as possible.

BellHawk Systems Corporation announces the availability of a new white paper "Artificial Intelligence and the Smart Industrial Warehouse." This white paper is available for download from the front page News section of www.BellHawk.com. In the popular press, there is much ado made about Artificial Intelligence (AI) being used in robots that buzz about high volume retail warehouses, automatically picking consumer products that are being shipped overnight in response to orders made over the Internet. But this misses all the ways that AI can be used to inexpensively improve the operation of industrial warehouses without a major investment in robots or other expensive materials handling equipment by providing the information and advice that managers, supervisors, and material handlers need to do their jobs efficiently. This white paper examines how AI based operations tracking and management systems, such as BellHawk, can be used to improve the efficiency of industrial warehouses, prevent mistakes, and enable customer orders to be shipped on time.

Word embeddings are a powerful approach for capturing semantic similarity among terms in a vocabulary. In this paper, we develop exponential family embeddings, a class of methods that extends the idea of word embeddings to other types of high-dimensional data. As examples, we studied neural data with real-valued observations, count data from a market basket analysis, and ratings data from a movie recommendation system. The main idea is to model each observation conditioned on a set of other observations. This set is called the context, and the way the context is defined is a modeling choice that depends on the problem. In language the context is the surrounding words; in neuroscience the context is close-by neurons; in market basket data the context is other items in the shopping cart. Each type of embedding model defines the context, the exponential family of conditional distributions, and how the latent embedding vectors are shared across data. We infer the embeddings with a scalable algorithm based on stochastic gradient descent. On all three applications - neural activity of zebrafish, users' shopping behavior, and movie ratings - we found exponential family embedding models to be more effective than other types of dimension reduction. They better reconstruct held-out data and find interesting qualitative structure.

THAT a computer program can repeatedly beat the world champion at Go, a complex board game, is a coup for the fast-moving field of artificial intelligence (AI). Another high-stakes game, however, is taking place behind the scenes, as firms compete to hire the smartest AI experts. Technology giants, including Google, Facebook, Microsoft and Baidu, are racing to expand their AI activities. Last year they spent some $8.5 billion on deals, says Quid, a data firm. That was four times more than in 2010.